Overview

Dataset statistics

Number of variables15
Number of observations2699720
Missing cells59605
Missing cells (%)0.1%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory309.0 MiB
Average record size in memory120.0 B

Variable types

Numeric11
Categorical4

Alerts

mission_id has a high cardinality: 263 distinct values High cardinality
geo_country has a high cardinality: 220 distinct values High cardinality
event_timestamp has a high cardinality: 2698313 distinct values High cardinality
mission_difficulty is highly correlated with mission_stars_collectedHigh correlation
mission_stars_collected is highly correlated with mission_difficulty and 3 other fieldsHigh correlation
day_auto_increment is highly correlated with days_played_in_monthHigh correlation
lifetime_played_runs is highly correlated with mission_stars_collected and 2 other fieldsHigh correlation
max_run_distance is highly correlated with mission_stars_collected and 1 other fieldsHigh correlation
total_purchases_virtual is highly correlated with virtual_currency_balanceHigh correlation
total_ads_watched is highly correlated with mission_stars_collected and 1 other fieldsHigh correlation
days_played_in_month is highly correlated with day_auto_incrementHigh correlation
virtual_currency_balance is highly correlated with total_purchases_virtualHigh correlation
day_auto_increment is highly correlated with total_purchases_virtual and 1 other fieldsHigh correlation
total_purchases_virtual is highly correlated with day_auto_increment and 1 other fieldsHigh correlation
days_played_in_month is highly correlated with day_auto_increment and 1 other fieldsHigh correlation
mission_stars_collected is highly correlated with lifetime_played_runsHigh correlation
day_auto_increment is highly correlated with days_played_in_monthHigh correlation
lifetime_played_runs is highly correlated with mission_stars_collectedHigh correlation
days_played_in_month is highly correlated with day_auto_incrementHigh correlation
day_auto_increment is highly correlated with total_purchases_virtual and 1 other fieldsHigh correlation
max_run_distance is highly correlated with days_played_in_monthHigh correlation
total_purchases_virtual is highly correlated with day_auto_increment and 1 other fieldsHigh correlation
days_played_in_month is highly correlated with day_auto_increment and 2 other fieldsHigh correlation
mission_stars_collected is highly skewed (γ1 = 158.1877012) Skewed
day_auto_increment is highly skewed (γ1 = 74.28247207) Skewed
total_purchases_virtual is highly skewed (γ1 = 284.0938017) Skewed
total_purchases_real is highly skewed (γ1 = 67.72103605) Skewed
days_played_in_month is highly skewed (γ1 = 130.1008803) Skewed
virtual_currency_balance is highly skewed (γ1 = 57.32590572) Skewed
event_timestamp is uniformly distributed Uniform
day_auto_increment has 1689025 (62.6%) zeros Zeros
total_purchases_virtual has 1322796 (49.0%) zeros Zeros
total_ads_watched has 1401357 (51.9%) zeros Zeros
total_purchases_real has 2676752 (99.1%) zeros Zeros
days_played_in_month has 1819428 (67.4%) zeros Zeros

Reproduction

Analysis started2022-05-23 14:23:54.476962
Analysis finished2022-05-23 14:33:28.176095
Duration9 minutes and 33.7 seconds
Software versionpandas-profiling v3.1.0
Download configurationconfig.json

Variables

df_index
Real number (ℝ≥0)

Distinct269972
Distinct (%)10.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean151174.3564
Minimum0
Maximum290201
Zeros10
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size20.6 MiB
2022-05-23T16:33:28.285803image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile13573
Q186030.75
median154024.5
Q3222069.25
95-th percentile276579
Maximum290201
Range290201
Interquartile range (IQR)136038.5

Descriptive statistics

Standard deviation82896.61638
Coefficient of variation (CV)0.5483510456
Kurtosis-1.099037267
Mean151174.3564
Median Absolute Deviation (MAD)68019.5
Skewness-0.1286024473
Sum4.081284334 × 1011
Variance6871849007
MonotonicityIncreasing
2022-05-23T16:33:28.403500image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
010
 
< 0.1%
19937810
 
< 0.1%
19938010
 
< 0.1%
19938110
 
< 0.1%
19938210
 
< 0.1%
19938310
 
< 0.1%
19938410
 
< 0.1%
19938510
 
< 0.1%
19938610
 
< 0.1%
19938710
 
< 0.1%
Other values (269962)2699620
> 99.9%
ValueCountFrequency (%)
010
< 0.1%
110
< 0.1%
210
< 0.1%
310
< 0.1%
410
< 0.1%
510
< 0.1%
610
< 0.1%
710
< 0.1%
810
< 0.1%
910
< 0.1%
ValueCountFrequency (%)
29020110
< 0.1%
29020010
< 0.1%
29019910
< 0.1%
29019810
< 0.1%
29019710
< 0.1%
29019610
< 0.1%
29019510
< 0.1%
29019410
< 0.1%
29019310
< 0.1%
29019210
< 0.1%

user_pseudo_id
Real number (ℝ≥0)

Distinct269972
Distinct (%)10.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean49976825.34
Minimum794
Maximum99999617
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size20.6 MiB
2022-05-23T16:33:28.540135image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum794
5-th percentile5012802
Q124958010
median50039583.5
Q374913214
95-th percentile94925500
Maximum99999617
Range99998823
Interquartile range (IQR)49955204

Descriptive statistics

Standard deviation28863326.06
Coefficient of variation (CV)0.5775342044
Kurtosis-1.2019779
Mean49976825.34
Median Absolute Deviation (MAD)24977416.5
Skewness-0.001803132403
Sum1.349234349 × 1014
Variance8.330915914 × 1014
MonotonicityNot monotonic
2022-05-23T16:33:28.659815image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1572515710
 
< 0.1%
3472328610
 
< 0.1%
7128463810
 
< 0.1%
6212917110
 
< 0.1%
1857557410
 
< 0.1%
9395611610
 
< 0.1%
7630722610
 
< 0.1%
7075255710
 
< 0.1%
6609311110
 
< 0.1%
643024810
 
< 0.1%
Other values (269962)2699620
> 99.9%
ValueCountFrequency (%)
79410
< 0.1%
94310
< 0.1%
103010
< 0.1%
251110
< 0.1%
272310
< 0.1%
299110
< 0.1%
319410
< 0.1%
384210
< 0.1%
548010
< 0.1%
621310
< 0.1%
ValueCountFrequency (%)
9999961710
< 0.1%
9999943210
< 0.1%
9999933410
< 0.1%
9999870110
< 0.1%
9999836710
< 0.1%
9999824010
< 0.1%
9999786810
< 0.1%
9999762010
< 0.1%
9999731010
< 0.1%
9999645210
< 0.1%

mission_id
Categorical

HIGH CARDINALITY

Distinct263
Distinct (%)< 0.1%
Missing1
Missing (%)< 0.1%
Memory size20.6 MiB
Mission3
279709 
Mission115
279560 
Mission86
271341 
Mission114
261628 
Mission19
261235 
Other values (258)
1346246 

Length

Max length10
Median length9
Mean length9.194731378
Min length8

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique36 ?
Unique (%)< 0.1%

Sample

1st rowMission94
2nd rowMission11
3rd rowMission6
4th rowMission3
5th rowMission114

Common Values

ValueCountFrequency (%)
Mission3279709
10.4%
Mission115279560
10.4%
Mission86271341
10.1%
Mission114261628
9.7%
Mission19261235
9.7%
Mission11260368
9.6%
Mission109256450
9.5%
Mission113256398
9.5%
Mission6249904
9.3%
Mission12188743
7.0%
Other values (253)134383
5.0%

Length

2022-05-23T16:33:28.772513image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
mission3279709
10.4%
mission115279560
10.4%
mission86271341
10.1%
mission114261628
9.7%
mission19261235
9.7%
mission11260368
9.6%
mission109256450
9.5%
mission113256398
9.5%
mission6249904
9.3%
mission12188743
7.0%
Other values (253)134383
5.0%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

mission_difficulty
Categorical

HIGH CORRELATION

Distinct3
Distinct (%)< 0.1%
Missing1
Missing (%)< 0.1%
Memory size20.6 MiB
1.0
1946778 
2.0
752908 
3.0
 
33

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2.0
2nd row1.0
3rd row1.0
4th row1.0
5th row1.0

Common Values

ValueCountFrequency (%)
1.01946778
72.1%
2.0752908
 
27.9%
3.033
 
< 0.1%
(Missing)1
 
< 0.1%

Length

2022-05-23T16:33:28.866263image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2022-05-23T16:33:28.926607image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
ValueCountFrequency (%)
1.01946778
72.1%
2.0752908
 
27.9%
3.033
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

mission_stars_collected
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
SKEWED

Distinct78
Distinct (%)< 0.1%
Missing1
Missing (%)< 0.1%
Infinite0
Infinite (%)0.0%
Mean7.509476357
Minimum0
Maximum1772
Zeros6201
Zeros (%)0.2%
Negative0
Negative (%)0.0%
Memory size20.6 MiB
2022-05-23T16:33:29.004399image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile3
Q14
median7
Q311
95-th percentile14
Maximum1772
Range1772
Interquartile range (IQR)7

Descriptive statistics

Standard deviation5.035307679
Coefficient of variation (CV)0.6705271366
Kurtosis55000.28863
Mean7.509476357
Median Absolute Deviation (MAD)3
Skewness158.1877012
Sum20273476
Variance25.35432342
MonotonicityNot monotonic
2022-05-23T16:33:29.115607image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
3485088
18.0%
6278386
10.3%
8261153
9.7%
4233907
8.7%
7216019
8.0%
5208218
7.7%
11194310
7.2%
14178627
 
6.6%
9177107
 
6.6%
12131572
 
4.9%
Other values (68)335332
12.4%
ValueCountFrequency (%)
06201
 
0.2%
116610
 
0.6%
27466
 
0.3%
3485088
18.0%
4233907
8.7%
5208218
7.7%
6278386
10.3%
7216019
8.0%
8261153
9.7%
9177107
 
6.6%
ValueCountFrequency (%)
17721
< 0.1%
17692
< 0.1%
17681
< 0.1%
17661
< 0.1%
17651
< 0.1%
17612
< 0.1%
17601
< 0.1%
17591
< 0.1%
2801
< 0.1%
2771
< 0.1%

day_auto_increment
Real number (ℝ)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
SKEWED
ZEROS

Distinct54
Distinct (%)< 0.1%
Missing10
Missing (%)< 0.1%
Infinite0
Infinite (%)0.0%
Mean1811.976636
Minimum-1
Maximum10000001
Zeros1689025
Zeros (%)62.6%
Negative7328
Negative (%)0.3%
Memory size20.6 MiB
2022-05-23T16:33:29.237786image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum-1
5-th percentile0
Q10
median0
Q31
95-th percentile3
Maximum10000001
Range10000002
Interquartile range (IQR)1

Descriptive statistics

Standard deviation134572.5713
Coefficient of variation (CV)74.26838106
Kurtosis5515.889744
Mean1811.976636
Median Absolute Deviation (MAD)0
Skewness74.28247207
Sum4891811444
Variance1.810977694 × 1010
MonotonicityNot monotonic
2022-05-23T16:33:29.357466image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
01689025
62.6%
1613906
 
22.7%
2204795
 
7.6%
387440
 
3.2%
442314
 
1.6%
522707
 
0.8%
612144
 
0.4%
-17328
 
0.3%
77064
 
0.3%
84230
 
0.2%
Other values (44)8757
 
0.3%
ValueCountFrequency (%)
-17328
 
0.3%
01689025
62.6%
1613906
 
22.7%
2204795
 
7.6%
387440
 
3.2%
442314
 
1.6%
522707
 
0.8%
612144
 
0.4%
77064
 
0.3%
84230
 
0.2%
ValueCountFrequency (%)
100000019
 
< 0.1%
1000000053
 
< 0.1%
9999999427
< 0.1%
581
 
< 0.1%
521
 
< 0.1%
511
 
< 0.1%
506
 
< 0.1%
461
 
< 0.1%
451
 
< 0.1%
441
 
< 0.1%

lifetime_played_runs
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION

Distinct285
Distinct (%)< 0.1%
Missing7965
Missing (%)0.3%
Infinite0
Infinite (%)0.0%
Mean6.175839554
Minimum0
Maximum632
Zeros5955
Zeros (%)0.2%
Negative0
Negative (%)0.0%
Memory size20.6 MiB
2022-05-23T16:33:29.476149image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q12
median4
Q38
95-th percentile19
Maximum632
Range632
Interquartile range (IQR)6

Descriptive statistics

Standard deviation7.939005078
Coefficient of variation (CV)1.285494062
Kurtosis183.4860265
Mean6.175839554
Median Absolute Deviation (MAD)2
Skewness7.600313068
Sum16623847
Variance63.02780163
MonotonicityNot monotonic
2022-05-23T16:33:29.584858image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2544242
20.2%
1418944
15.5%
3329511
12.2%
4238250
8.8%
5191507
 
7.1%
6158984
 
5.9%
7129347
 
4.8%
8105996
 
3.9%
984545
 
3.1%
1069151
 
2.6%
Other values (275)421278
15.6%
ValueCountFrequency (%)
05955
 
0.2%
1418944
15.5%
2544242
20.2%
3329511
12.2%
4238250
8.8%
5191507
 
7.1%
6158984
 
5.9%
7129347
 
4.8%
8105996
 
3.9%
984545
 
3.1%
ValueCountFrequency (%)
6321
 
< 0.1%
45410
< 0.1%
4081
 
< 0.1%
4061
 
< 0.1%
4031
 
< 0.1%
4023
 
< 0.1%
3891
 
< 0.1%
3872
 
< 0.1%
3862
 
< 0.1%
3853
 
< 0.1%

max_run_distance
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION

Distinct9702
Distinct (%)0.4%
Missing7965
Missing (%)0.3%
Infinite0
Infinite (%)0.0%
Mean2387.397828
Minimum0
Maximum57880
Zeros5981
Zeros (%)0.2%
Negative0
Negative (%)0.0%
Memory size20.6 MiB
2022-05-23T16:33:29.701546image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1140
Q11502
median2135
Q32907
95-th percentile4632
Maximum57880
Range57880
Interquartile range (IQR)1405

Descriptive statistics

Standard deviation1197.57174
Coefficient of variation (CV)0.5016221956
Kurtosis24.05959994
Mean2387.397828
Median Absolute Deviation (MAD)677
Skewness2.252180527
Sum6426290040
Variance1434178.073
MonotonicityNot monotonic
2022-05-23T16:33:29.814750image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
05981
 
0.2%
12241710
 
0.1%
11451707
 
0.1%
11671705
 
0.1%
11601702
 
0.1%
13111700
 
0.1%
12011694
 
0.1%
11811684
 
0.1%
12341682
 
0.1%
13121673
 
0.1%
Other values (9692)2670517
98.9%
(Missing)7965
 
0.3%
ValueCountFrequency (%)
05981
0.2%
1031
 
< 0.1%
1043
 
< 0.1%
1082
 
< 0.1%
1132
 
< 0.1%
1161
 
< 0.1%
1172
 
< 0.1%
1183
 
< 0.1%
1194
 
< 0.1%
1222
 
< 0.1%
ValueCountFrequency (%)
578809
< 0.1%
284631
 
< 0.1%
273429
< 0.1%
2645210
< 0.1%
2464810
< 0.1%
223714
 
< 0.1%
217441
 
< 0.1%
208074
 
< 0.1%
201532
 
< 0.1%
200533
 
< 0.1%

total_purchases_virtual
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
SKEWED
ZEROS

Distinct5377
Distinct (%)0.2%
Missing8062
Missing (%)0.3%
Infinite0
Infinite (%)0.0%
Mean51568.88489
Minimum0
Maximum1245420000
Zeros1322796
Zeros (%)49.0%
Negative0
Negative (%)0.0%
Memory size20.6 MiB
2022-05-23T16:33:29.941451image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median500
Q34000
95-th percentile11000
Maximum1245420000
Range1245420000
Interquartile range (IQR)4000

Descriptive statistics

Standard deviation3304984.35
Coefficient of variation (CV)64.08873019
Kurtosis96175.47529
Mean51568.88489
Median Absolute Deviation (MAD)500
Skewness284.0938017
Sum1.388058016 × 1011
Variance1.092292155 × 1013
MonotonicityNot monotonic
2022-05-23T16:33:30.064123image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
01322796
49.0%
1500315472
 
11.7%
4000128332
 
4.8%
550087348
 
3.2%
300080625
 
3.0%
50065728
 
2.4%
450064536
 
2.4%
600058395
 
2.2%
100048742
 
1.8%
200045576
 
1.7%
Other values (5367)474108
 
17.6%
ValueCountFrequency (%)
01322796
49.0%
50065728
 
2.4%
100048742
 
1.8%
1500315472
 
11.7%
200045576
 
1.7%
250037774
 
1.4%
300080625
 
3.0%
350024003
 
0.9%
4000128332
 
4.8%
450064536
 
2.4%
ValueCountFrequency (%)
12454200002
 
< 0.1%
12435500008
< 0.1%
10936500003
 
< 0.1%
10835500001
 
< 0.1%
4800000009
< 0.1%
4700000001
 
< 0.1%
4635470007
< 0.1%
4345260002
 
< 0.1%
39385600010
< 0.1%
22354800010
< 0.1%

total_ads_watched
Real number (ℝ≥0)

HIGH CORRELATION
ZEROS

Distinct170
Distinct (%)< 0.1%
Missing8058
Missing (%)0.3%
Infinite0
Infinite (%)0.0%
Mean2.432695487
Minimum0
Maximum239
Zeros1401357
Zeros (%)51.9%
Negative0
Negative (%)0.0%
Memory size20.6 MiB
2022-05-23T16:33:30.181809image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q33
95-th percentile11
Maximum239
Range239
Interquartile range (IQR)3

Descriptive statistics

Standard deviation5.001427299
Coefficient of variation (CV)2.055919997
Kurtosis82.39498735
Mean2.432695487
Median Absolute Deviation (MAD)0
Skewness5.929526616
Sum6547994
Variance25.01427503
MonotonicityNot monotonic
2022-05-23T16:33:30.296502image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
01401357
51.9%
1320961
 
11.9%
2217961
 
8.1%
3161983
 
6.0%
4122860
 
4.6%
593524
 
3.5%
671118
 
2.6%
755087
 
2.0%
842428
 
1.6%
933425
 
1.2%
Other values (160)170958
 
6.3%
ValueCountFrequency (%)
01401357
51.9%
1320961
 
11.9%
2217961
 
8.1%
3161983
 
6.0%
4122860
 
4.6%
593524
 
3.5%
671118
 
2.6%
755087
 
2.0%
842428
 
1.6%
933425
 
1.2%
ValueCountFrequency (%)
2391
 
< 0.1%
2351
 
< 0.1%
2301
 
< 0.1%
2251
 
< 0.1%
2201
 
< 0.1%
2171
 
< 0.1%
2051
 
< 0.1%
2021
 
< 0.1%
2013
< 0.1%
1981
 
< 0.1%

total_purchases_real
Real number (ℝ≥0)

SKEWED
ZEROS

Distinct606
Distinct (%)< 0.1%
Missing8120
Missing (%)0.3%
Infinite0
Infinite (%)0.0%
Mean0.02275532026
Minimum0
Maximum133.93
Zeros2676752
Zeros (%)99.1%
Negative0
Negative (%)0.0%
Memory size20.6 MiB
2022-05-23T16:33:30.406209image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0
Maximum133.93
Range133.93
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.6200074983
Coefficient of variation (CV)27.246705
Kurtosis7016.985764
Mean0.02275532026
Median Absolute Deviation (MAD)0
Skewness67.72103605
Sum61248.22
Variance0.3844092979
MonotonicityNot monotonic
2022-05-23T16:33:30.524891image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
02676752
99.1%
1.992139
 
0.1%
0.481158
 
< 0.1%
0.49814
 
< 0.1%
4.98661
 
< 0.1%
0.99598
 
< 0.1%
0.37445
 
< 0.1%
2.99420
 
< 0.1%
3.99356
 
< 0.1%
0.11261
 
< 0.1%
Other values (596)7996
 
0.3%
(Missing)8120
 
0.3%
ValueCountFrequency (%)
02676752
99.1%
0.11261
 
< 0.1%
0.135
 
< 0.1%
0.1425
 
< 0.1%
0.2271
 
< 0.1%
0.272
 
< 0.1%
0.31
 
< 0.1%
0.3148
 
< 0.1%
0.3229
 
< 0.1%
0.33155
 
< 0.1%
ValueCountFrequency (%)
133.931
 
< 0.1%
96.694
 
< 0.1%
94.71
 
< 0.1%
92.533
 
< 0.1%
79.916
< 0.1%
78.764
 
< 0.1%
73.943
 
< 0.1%
73.9310
< 0.1%
67.8310
< 0.1%
64.976
< 0.1%

geo_country
Categorical

HIGH CARDINALITY

Distinct220
Distinct (%)< 0.1%
Missing3482
Missing (%)0.1%
Memory size20.6 MiB
United States
450568 
Mexico
342940 
Brazil
 
132349
France
 
112590
Russia
 
108295
Other values (215)
1549496 

Length

Max length24
Median length7
Mean length8.137137374
Min length4

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowHonduras
2nd rowHonduras
3rd rowHonduras
4th rowHonduras
5th rowHonduras

Common Values

ValueCountFrequency (%)
United States450568
 
16.7%
Mexico342940
 
12.7%
Brazil132349
 
4.9%
France112590
 
4.2%
Russia108295
 
4.0%
India96678
 
3.6%
Germany91358
 
3.4%
Turkey83295
 
3.1%
United Kingdom83167
 
3.1%
Colombia68467
 
2.5%
Other values (210)1126531
41.7%

Length

2022-05-23T16:33:30.645569image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
united540059
16.2%
states450568
 
13.5%
mexico342940
 
10.3%
brazil132349
 
4.0%
france112590
 
3.4%
russia108295
 
3.2%
india96678
 
2.9%
germany91358
 
2.7%
turkey83295
 
2.5%
kingdom83167
 
2.5%
Other values (249)1292045
38.8%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

days_played_in_month
Real number (ℝ)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
SKEWED
ZEROS

Distinct28
Distinct (%)< 0.1%
Missing7974
Missing (%)0.3%
Infinite0
Infinite (%)0.0%
Mean591.1845683
Minimum-1
Maximum9999999
Zeros1819428
Zeros (%)67.4%
Negative7657
Negative (%)0.3%
Memory size20.6 MiB
2022-05-23T16:33:30.748798image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum-1
5-th percentile0
Q10
median0
Q31
95-th percentile2
Maximum9999999
Range10000000
Interquartile range (IQR)1

Descriptive statistics

Standard deviation76854.39583
Coefficient of variation (CV)130.0006799
Kurtosis16924.25162
Mean591.1845683
Median Absolute Deviation (MAD)0
Skewness130.1008803
Sum1591318697
Variance5906598158
MonotonicityNot monotonic
2022-05-23T16:33:30.848531image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=28)
ValueCountFrequency (%)
01819428
67.4%
1593513
 
22.0%
2166533
 
6.2%
359488
 
2.2%
424227
 
0.9%
510926
 
0.4%
-17657
 
0.3%
64881
 
0.2%
72318
 
0.1%
81187
 
< 0.1%
Other values (18)1588
 
0.1%
(Missing)7974
 
0.3%
ValueCountFrequency (%)
-17657
 
0.3%
01819428
67.4%
1593513
 
22.0%
2166533
 
6.2%
359488
 
2.2%
424227
 
0.9%
510926
 
0.4%
64881
 
0.2%
72318
 
0.1%
81187
 
< 0.1%
ValueCountFrequency (%)
9999999159
< 0.1%
251
 
< 0.1%
243
 
< 0.1%
233
 
< 0.1%
222
 
< 0.1%
211
 
< 0.1%
205
 
< 0.1%
193
 
< 0.1%
187
 
< 0.1%
1713
 
< 0.1%

virtual_currency_balance
Real number (ℝ≥0)

HIGH CORRELATION
SKEWED

Distinct46931
Distinct (%)1.7%
Missing7965
Missing (%)0.3%
Infinite0
Infinite (%)0.0%
Mean497783.2425
Minimum0
Maximum2146000000
Zeros216
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size20.6 MiB
2022-05-23T16:33:30.962229image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile603
Q14014
median5648
Q36992
95-th percentile11459
Maximum2146000000
Range2146000000
Interquartile range (IQR)2978

Descriptive statistics

Standard deviation21832254.87
Coefficient of variation (CV)43.85895909
Kurtosis3601.872758
Mean497783.2425
Median Absolute Deviation (MAD)1526
Skewness57.32590572
Sum1.339910532 × 1012
Variance4.766473528 × 1014
MonotonicityNot monotonic
2022-05-23T16:33:31.071934image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
55791862
 
0.1%
55701841
 
0.1%
55541834
 
0.1%
55451801
 
0.1%
55711793
 
0.1%
55741793
 
0.1%
55591769
 
0.1%
55621769
 
0.1%
55661766
 
0.1%
55481759
 
0.1%
Other values (46921)2673768
99.0%
(Missing)7965
 
0.3%
ValueCountFrequency (%)
0216
< 0.1%
1166
< 0.1%
2183
< 0.1%
3174
< 0.1%
4177
< 0.1%
5182
< 0.1%
6159
< 0.1%
7181
< 0.1%
8165
< 0.1%
9155
< 0.1%
ValueCountFrequency (%)
21460000009
< 0.1%
20000000004
 
< 0.1%
19999900001
 
< 0.1%
19976500002
 
< 0.1%
19961500003
 
< 0.1%
19961400005
< 0.1%
199562000010
< 0.1%
199525000010
< 0.1%
19941100005
< 0.1%
19941000008
< 0.1%

event_timestamp
Categorical

HIGH CARDINALITY
UNIFORM

Distinct2698313
Distinct (%)99.9%
Missing1
Missing (%)< 0.1%
Memory size20.6 MiB
2022-02-13 17:40:39.244 UTC
 
3
2022-02-26 19:17:49.008 UTC
 
2
2022-02-04 23:57:35.289 UTC
 
2
2022-02-17 17:52:47.03 UTC
 
2
2022-02-25 12:51:13.228 UTC
 
2
Other values (2698308)
2699708 

Length

Max length30
Median length27
Mean length26.8916328
Min length23

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique2696908 ?
Unique (%)99.9%

Sample

1st row2022-03-02 15:46:04.025 UTC
2nd row2022-03-14 02:00:06.716 UTC
3rd row2022-03-14 02:01:13.236 UTC
4th row2022-03-05 17:56:06.473 UTC
5th row2022-03-05 17:52:48.383 UTC

Common Values

ValueCountFrequency (%)
2022-02-13 17:40:39.244 UTC3
 
< 0.1%
2022-02-26 19:17:49.008 UTC2
 
< 0.1%
2022-02-04 23:57:35.289 UTC2
 
< 0.1%
2022-02-17 17:52:47.03 UTC2
 
< 0.1%
2022-02-25 12:51:13.228 UTC2
 
< 0.1%
2022-02-19 18:47:18.364 UTC2
 
< 0.1%
2022-02-13 10:57:14.24 UTC2
 
< 0.1%
2022-02-22 21:27:40.712 UTC2
 
< 0.1%
2022-02-02 15:15:35.008 UTC2
 
< 0.1%
2022-02-23 10:16:06.155 UTC2
 
< 0.1%
Other values (2698303)2699698
> 99.9%

Length

2022-05-23T16:33:31.402557image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
utc2699719
33.3%
2022-02-27103257
 
1.3%
2022-02-26101557
 
1.3%
2022-02-20101513
 
1.3%
2022-02-2597943
 
1.2%
2022-02-1997462
 
1.2%
2022-02-2296591
 
1.2%
2022-02-1395432
 
1.2%
2022-02-2195224
 
1.2%
2022-02-0694783
 
1.2%
Other values (2655782)4515676
55.8%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

Interactions

2022-05-23T16:33:09.303303image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-05-23T16:31:46.110573image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-05-23T16:31:54.753109image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-05-23T16:32:03.351192image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-05-23T16:32:11.830643image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-05-23T16:32:20.186405image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-05-23T16:32:28.606499image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-05-23T16:32:37.386683image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-05-23T16:32:45.427285image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-05-23T16:32:53.408084image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-05-23T16:33:01.056726image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-05-23T16:33:10.076741image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-05-23T16:31:47.055567image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-05-23T16:31:55.518064image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-05-23T16:32:04.117156image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-05-23T16:32:12.600090image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-05-23T16:32:20.994766image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-05-23T16:32:29.420336image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-05-23T16:32:38.139683image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-05-23T16:32:46.185761image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-05-23T16:32:54.103225image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-05-23T16:33:01.845616image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-05-23T16:33:10.805310image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-05-23T16:31:47.828019image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-05-23T16:31:56.288018image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-05-23T16:32:04.844212image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-05-23T16:32:13.337624image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-05-23T16:32:21.759736image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-05-23T16:32:30.216221image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-05-23T16:32:38.856274image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-05-23T16:32:46.910835image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-05-23T16:32:54.774441image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-05-23T16:33:02.592645image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-05-23T16:33:11.547832image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-05-23T16:31:48.599969image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-05-23T16:31:57.076415image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-05-23T16:32:05.587248image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-05-23T16:32:14.062686image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-05-23T16:32:22.518706image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-05-23T16:32:31.016095image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-05-23T16:32:39.579863image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-05-23T16:32:47.638901image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-05-23T16:32:55.462601image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-05-23T16:33:03.348645image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-05-23T16:33:12.283420image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-05-23T16:31:49.368912image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-05-23T16:31:57.870292image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-05-23T16:32:06.336260image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-05-23T16:32:14.801721image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-05-23T16:32:23.267731image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-05-23T16:32:31.836919image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-05-23T16:32:40.306427image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-05-23T16:32:48.363976image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-05-23T16:32:56.148274image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-05-23T16:33:04.106126image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-05-23T16:33:13.035421image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-05-23T16:31:50.148827image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-05-23T16:31:58.663185image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-05-23T16:32:07.101214image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-05-23T16:32:15.575651image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-05-23T16:32:24.035187image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-05-23T16:32:32.631816image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-05-23T16:32:41.040984image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-05-23T16:32:49.101509image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-05-23T16:32:56.863868image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-05-23T16:33:04.860629image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-05-23T16:33:13.771958image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-05-23T16:31:50.911308image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-05-23T16:31:59.439616image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-05-23T16:32:07.901094image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-05-23T16:32:16.346604image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-05-23T16:32:24.761245image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-05-23T16:32:33.421720image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-05-23T16:32:41.752589image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-05-23T16:32:49.823617image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-05-23T16:32:57.549050image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-05-23T16:33:05.602164image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-05-23T16:33:14.512976image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-05-23T16:31:51.694741image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-05-23T16:32:00.248453image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-05-23T16:32:08.699467image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-05-23T16:32:17.118539image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-05-23T16:32:25.524220image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-05-23T16:32:34.216594image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-05-23T16:32:42.472663image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-05-23T16:32:50.552202image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-05-23T16:32:58.247197image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-05-23T16:33:06.348674image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-05-23T16:33:15.250019image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-05-23T16:31:52.456211image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-05-23T16:32:01.024389image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-05-23T16:32:09.465436image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-05-23T16:32:17.868559image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-05-23T16:32:26.274214image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-05-23T16:32:35.001517image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-05-23T16:32:43.175290image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-05-23T16:32:51.266293image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-05-23T16:32:58.901460image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-05-23T16:33:07.076739image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-05-23T16:33:15.990544image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-05-23T16:31:53.219689image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-05-23T16:32:01.825247image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-05-23T16:32:10.278767image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-05-23T16:32:18.638514image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-05-23T16:32:27.015739image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-05-23T16:32:35.799888image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-05-23T16:32:43.928293image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-05-23T16:32:51.992351image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-05-23T16:32:59.605589image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-05-23T16:33:07.798807image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-05-23T16:33:16.714607image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-05-23T16:31:53.982171image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-05-23T16:32:02.596197image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-05-23T16:32:11.044240image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-05-23T16:32:19.395995image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-05-23T16:32:27.787183image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-05-23T16:32:36.635672image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-05-23T16:32:44.662332image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-05-23T16:32:52.716428image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-05-23T16:33:00.289777image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-05-23T16:33:08.539332image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Correlations

2022-05-23T16:33:31.515296image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Spearman's ρ

The Spearman's rank correlation coefficient (ρ) is a measure of monotonic correlation between two variables, and is therefore better in catching nonlinear monotonic correlations than Pearson's r. It's value lies between -1 and +1, -1 indicating total negative monotonic correlation, 0 indicating no monotonic correlation and 1 indicating total positive monotonic correlation.

To calculate ρ for two variables X and Y, one divides the covariance of the rank variables of X and Y by the product of their standard deviations.
2022-05-23T16:33:31.714762image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Pearson's r

The Pearson's correlation coefficient (r) is a measure of linear correlation between two variables. It's value lies between -1 and +1, -1 indicating total negative linear correlation, 0 indicating no linear correlation and 1 indicating total positive linear correlation. Furthermore, r is invariant under separate changes in location and scale of the two variables, implying that for a linear function the angle to the x-axis does not affect r.

To calculate r for two variables X and Y, one divides the covariance of X and Y by the product of their standard deviations.
2022-05-23T16:33:31.921211image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Kendall's τ

Similarly to Spearman's rank correlation coefficient, the Kendall rank correlation coefficient (τ) measures ordinal association between two variables. It's value lies between -1 and +1, -1 indicating total negative correlation, 0 indicating no correlation and 1 indicating total positive correlation.

To calculate τ for two variables X and Y, one determines the number of concordant and discordant pairs of observations. τ is given by the number of concordant pairs minus the discordant pairs divided by the total number of pairs.
2022-05-23T16:33:32.122672image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Phik (φk)

Phik (φk) is a new and practical correlation coefficient that works consistently between categorical, ordinal and interval variables, captures non-linear dependency and reverts to the Pearson correlation coefficient in case of a bivariate normal input distribution. There is extensive documentation available here.

Missing values

2022-05-23T16:33:17.705956image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
A simple visualization of nullity by column.
2022-05-23T16:33:19.385985image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2022-05-23T16:33:24.269008image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.
2022-05-23T16:33:25.110757image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
The dendrogram allows you to more fully correlate variable completion, revealing trends deeper than the pairwise ones visible in the correlation heatmap.

Sample

First rows

df_indexuser_pseudo_idmission_idmission_difficultymission_stars_collectedday_auto_incrementlifetime_played_runsmax_run_distancetotal_purchases_virtualtotal_ads_watchedtotal_purchases_realgeo_countrydays_played_in_monthvirtual_currency_balanceevent_timestamp
0015725157Mission942.03.00.02.01763.02000.00.00.0Honduras0.05000.02022-03-02 15:46:04.025 UTC
1015725157Mission111.08.03.019.02585.07000.00.00.0Honduras3.02446.02022-03-14 02:00:06.716 UTC
2015725157Mission61.011.03.020.02585.07000.00.00.0Honduras3.02752.02022-03-14 02:01:13.236 UTC
3015725157Mission31.07.02.012.02266.07000.00.00.0Honduras2.01306.02022-03-05 17:56:06.473 UTC
4015725157Mission1141.06.02.09.02266.03500.00.00.0Honduras2.04284.02022-03-05 17:52:48.383 UTC
5015725157Mission862.012.03.021.02585.07000.00.00.0Honduras3.03322.02022-03-14 02:03:20.305 UTC
6015725157Mission1151.05.00.08.02266.03500.00.00.0Honduras0.03561.02022-03-02 15:54:49.526 UTC
7015725157Mission122.014.03.023.02585.08500.00.00.0Honduras3.02820.02022-03-14 02:05:01.562 UTC
8015725157Mission1091.04.00.03.02266.02000.00.00.0Honduras0.04238.02022-03-02 15:46:51.889 UTC
9015725157Mission192.08.03.019.02585.07000.00.00.0Honduras3.02446.02022-03-14 02:00:19.959 UTC

Last rows

df_indexuser_pseudo_idmission_idmission_difficultymission_stars_collectedday_auto_incrementlifetime_played_runsmax_run_distancetotal_purchases_virtualtotal_ads_watchedtotal_purchases_realgeo_countrydays_played_in_monthvirtual_currency_balanceevent_timestamp
269971029020157543559Mission31.06.01.04.01380.05500.02.00.0United Kingdom1.01587.02022-02-12 19:07:47.398 UTC
269971129020157543559Mission192.09.01.012.01740.05500.012.00.0United Kingdom1.010786.02022-02-12 19:27:31.618 UTC
269971229020157543559Mission111.09.01.012.01740.05500.011.00.0United Kingdom1.010786.02022-02-12 19:25:58.911 UTC
269971329020157543559Mission61.08.01.08.01740.05500.06.00.0United Kingdom1.03017.02022-02-12 19:15:31.415 UTC
269971429020157543559Mission1141.06.01.04.01380.05500.02.00.0United Kingdom1.01587.02022-02-12 19:06:39.925 UTC
269971529020157543559Mission1131.05.01.03.01380.05500.01.00.0United Kingdom1.0645.02022-02-12 19:04:37.206 UTC
269971629020157543559Mission1091.04.01.02.01380.00.00.00.0United Kingdom1.05667.02022-02-12 19:01:39.216 UTC
269971729020157543559Mission1151.03.00.01.01380.00.00.00.0United Kingdom0.05588.02022-02-11 20:29:04.483 UTC
269971829020157543559Mission862.012.01.014.02801.05500.014.00.0United Kingdom1.012680.02022-02-12 19:38:52.784 UTC
269971929020157543559Mission101.014.02.021.04662.021000.022.00.0United Kingdom2.02628.02022-02-13 14:49:04.674 UTC